Use case · NPS deep dive
NPS is a number. The verbatims are the story.
Your NPS is +32. Now what?
Most teams stop at the score. They track it, report it, and feel good or bad about it. But the number alone tells you nothing about what to do next.
The answers are in the verbatims — the open-text responses where customers explain their score. “The product is great but the support is terrible.” “I’d recommend it if the reporting were better.” “Switched to a competitor last month.”
These verbatims are the richest data in your NPS programme. And almost nobody analyses them at depth.
InsightNarrator runs five analysis techniques on every NPS dataset, automatically:
- ABSA — Which aspects of the experience drive promoter and detractor scores.
- Sentiment Driver Analysis — What changes the score.
- Feedback Classification — How many “detractor” comments are actually feature requests.
- Pain Point Discovery — The friction points that prevent passives from becoming promoters.
- Loyalty Driver Analysis — Why promoters advocate.
NPS overview
4,200 responses
+32
- Support response time−0.71Detractor driver
- Reporting limitations23%Passive blocker
- “It just works”71%Promoter theme
The NPS trap
Why a number without context is dangerous
An NPS of +42 looks good on a dashboard. But here’s what the number hides.
+42 overall, but −8 in your largest enterprise segment.
The aggregate masks a retention crisis.
+42 this quarter, up from +38.
But you changed the survey timing and the distribution channel. The improvement isn’t real.
+42 with 60% passives.
Your customers aren’t detractors, but they’re not advocates either. They’re one competitor pitch away from switching.
+42 with 90% of promoters giving no verbatim reason.
You don’t know why they like you. How do you protect what you can’t identify?
The score tells you where you stand. The verbatims tell you why — and what to do about it.
“We spent two years trying to move our NPS from +28 to +35. We hit +35 the quarter after we started analysing verbatims with InsightNarrator. Not because the tool changed the score — it changed what we fixed.”
The five analyses
Five techniques, run automatically on every NPS dataset
Analysis 1
ABSA — which aspects drive your score
ABSA breaks down the NPS verbatims into specific aspects of the experience and scores sentiment per aspect. Instead of “overall, customers are moderately positive,” you get an aspect-level split between promoters and detractors.
| Aspect | Promoters | Detractors | Delta |
|---|---|---|---|
| Product Quality | +0.81 | +0.22 | +0.59 |
| Customer Support | +0.34 | −0.71 | +1.05 |
| Onboarding | +0.52 | −0.38 | +0.90 |
| Pricing | +0.12 | −0.55 | +0.67 |
| Reporting | +0.45 | −0.15 | +0.60 |
Analysis 2
Sentiment Driver Analysis — what causes the score to change
Driver analysis identifies the variables that most strongly correlate with NPS shifts, ranking each driver by influence on score variance.
- 1. Support response time34%
- 2. Number of features used22%
- 3. Time since last interaction15%
- 4. Billing clarity11%
- 5. Onboarding completion8%
Share of NPS score variance explained.
Analysis 3
Feedback Classification — what detractors are really saying
Classification sorts every verbatim into categories, split by NPS band.
| Category | Promoters | Passives | Detractors |
|---|---|---|---|
| Praise (Product) | 340 | 45 | 2 |
| Feature Request | 28 | 67 | 41 |
| Complaint (Support) | 5 | 22 | 89 |
| Complaint (Pricing) | 3 | 18 | 34 |
| Churn Signal | 0 | 4 | 28 |
| Comparison (Competitor) | 2 | 8 | 19 |
| Question | 12 | 15 | 3 |
Analysis 4
Pain Point Discovery — what prevents promoters
Pain point analysis surfaces the friction hiding in passive verbatims.
Reporting limitations
23% of passives“I can’t get the data I need without exporting to Excel.”
The #1 reason passives don’t become promoters.
Mobile experience
15% of passives“The mobile app is slow and missing features.”
Integration gaps
11% of passives“Doesn’t connect to our CRM.”
Analysis 5
Loyalty Driver Analysis — why promoters recommend you
Loyalty driver analysis isolates the factors that create genuine advocacy, not just satisfaction.
- 1. Product reliability71%
“It just works.”
- 2. Time savings58%
“Saves me 3 hours per week.”
- 3. Support quality44%
“Support team actually solves problems.”
- 4. Team collaboration31%
“My whole team uses it.”
Share of promoters mentioning each theme.
Worked example
A B2B SaaS company, 12 months, real numbers
Starting point
NPS +28. 40% detractors. No systematic verbatim analysis.
Month 1
Uploaded 4,200 NPS responses from the past 18 months. Ran all five analyses.
Month 2
Based on ABSA and driver analysis, invested in support response time (average dropped from 14 hours to 4 hours).
Month 4
Based on pain point discovery, launched a reporting redesign (the #1 passive blocker).
Month 6
Based on classification, routed 41 “feature request” detractors to the product team. 12 became roadmap items.
Month 9
NPS reached +41. Detractor rate dropped from 40% to 24%.
Month 12
NPS stabilised at +44. Promoter rate hit 61%. Churn down 3.2 percentage points.
At a glance
- NPS
- +28 → +44
- Detractors
- 40% → 24%
- Promoters
- → 61%
- Churn
- −3.2 pts
The score improved because they stopped optimising the number and started fixing what the verbatims revealed.
Comparison
Why this is different from NPS dashboards
| Traditional NPS tool | InsightNarrator |
|---|---|
| Tracks the score | Tracks the score and explains it |
| Manual verbatim tagging | Automated, governed classification |
| “Detractor” is a label | “Detractor” is a starting point for 5 analyses |
| Theme tracking is manual and inconsistent | ABSA uses your taxonomy — consistent across survey waves |
| No connection to business risk | C-Risk identifies which detractors are about to escalate |
| Reports every quarter | Analyse on demand, as often as you import data |
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